R: Stepwise estimation tools

stepwise {fixest} | R Documentation

## Stepwise estimation tools

### Description

Functions to perform stepwise estimations.

### Usage

    sw(...)

    csw(...)

    sw0(...)

    csw0(...)

### Arguments

`...` | Represents formula variables to be added in a stepwise fashion to an estimation.

### Details

To include multiple independent variables, you need to use the stepwise functions. There are 4 stepwise functions: sw, sw0, csw, csw0. Let's explain that.

Assume you have the following formula: `fml = y ~ x1 + sw(x2, x3)`. The stepwise function `sw` will estimate the following two models: `y ~ x1 + x2` and `y ~ x1 + x3`. That is, each element in `sw()` is sequentially, and separately, added to the formula. Would have you used `sw0` in lieu of `sw`, then the model `y ~ x1` would also have been estimated. The `0` in the name implies that the model without any stepwise element will also be estimated.

Finally, the prefix `c` means cumulative: each stepwise element is added to the next. That is, `fml = y ~ x1 + csw(x2, x3)` would lead to the following models `y ~ x1 + x2` and `y ~ x1 + x2 + x3`. The `0` has the same meaning and would also lead to the model without the stepwise elements to be estimated: in other words, `fml = y ~ x1 + csw0(x2, x3)` leads to the following three models: `y ~ x1`, `y ~ x1 + x2` and `y ~ x1 + x2 + x3`.

### Examples

    base = iris
    names(base) = c("y", "x1", "x2", "x3", "species")

    # Regular stepwise
    feols(y ~ sw(x1, x2, x3), base)

    # Cumulative stepwise
    feols(y ~ csw(x1, x2, x3), base)

    # Using the 0
    feols(y ~ x1 + x2 + sw0(x3), base)

[Package fixest version 0.10.0 Index]